This repository contains all the necessary code to set up Label Studio for obtaining labeled data for business insights and model monitoring purposes, specifically tailored for NACE classification (NACE rev 2/rev 2.1). By utilizing this project, you can enhance the quality of activity classification procedures and improve model performance evaluation with lightning-fast labeling and annotation.
Before getting started, ensure you have the following prerequisites:
- Python 3.10
- Label Studio deployed and accessible for use (either locally or on a server)
- (Optional) Label Studio configured to be connected with an S3 storage system for backup, storage, and long-term annotation campaigns
Additionally, you will need the following:
- Required Python libraries (refer to
requirements.txtfor details)
To create a UI template for your specific classification problem:
- Clone this repository to your local machine.
- Install the required Python dependencies listed in
requirements.txt. - Configure Label Studio according to your specific use case and requirements.
- Utilize the provided XML-based UI template in
taxonomy.xmldirectly created fromcreate_template.pyas inspiration to build your customized interface.
Powered by S3: This project is empowered by S3 for efficient storage and retrieval of labeled data.
Compatible Open Source Project: Label Studio for NACE Classification is compatible with other open-source projects, fostering collaboration and interoperability within the community.
This project is licensed under the Apache License, promoting collaboration and free usage.
Feel free to explore, contribute, and adapt this project to your needs! If you encounter any issues or have suggestions for improvement, don't hesitate to reach out or submit a pull request. Happy lightning-fast labeling and annotation! 🏷️